The object recognition community studies class imbalance using real-world datasets or distributions that approximate real-world imbalance [1]}, [2]}, [3]}. [1]} state that two distributions can be used: linear and step imbalance (defined in our methodology Section ). At large-scale, datasets with many samples and classes tend to follow a long-tail distribution [3]}, [6]}, [7]}, with most of the classes occurring with small frequency and a few classes occurring with high frequency. Our work primarily focuses on FSL methods (e.g. ProtoNet, MAML) that were originally designed to work with low number of samples (i.e. at the tail-end of the distribution) and we do not consider the case of large sample numbers within a task.
